In the era of the Internet of Things (IoT), the retrieval of relevant medical information has become essential for efficient clinical decision-making. This paper introduces MedFusionRank, a novel approach to zero-shot medical information retrieval (MIR) that combines the strengths of pre-trained language models and statistical methods while addressing their limitations. The proposed approach leverages a pre-trained BERT-style model to extract compact yet informative keywords. These keywords are then enriched with domain knowledge by linking them to conceptual entities within a medical knowledge graph. Experimental evaluations on medical datasets demonstrate MedFusion Rank's superior performance over existing methods, with promising results with a variety of evaluation metrics. MedFusionRank demonstrates efficacy in retrieving relevant information, even from short or single-term queries.

本文介绍了MedFusionRank，一种新颖的零样本医学信息检索（MIR）方法，它结合了预训练语言模型和统计方法的优势，并解决了它们的局限性。该方法利用预训练的BERT风格模型提取简洁但信息丰富的关键词，然后通过将这些关键词与医学知识图中的概念实体链接，进一步丰富其领域知识。针对医学数据集的实验评估表明MedFusionRank相比现有方法表现优越，在多种评估指标上具有有希望的结果。MedFusionRank在检索相关信息方面表现出有效性，即使是来自于短查询或单个术语。

通过知识图谱嵌入进行零样本医学信息检索